At Google, we love data, and we love using it to solve problems, big and small. Our CEO, Sundar Pichai, recently explained, "machine learning is a core, transformative way by which we’re rethinking how we’re doing everything." He has adopted a firm belief that both data—and machine learning-based analyses that rely on it—will transform how all businesses operate and engage with customers.

As more and more businesses realize how data can help them unlock insights and gain competitive advantages, data engineering roles have become more sought after. In fact, a recent Forrester report describes how data engineers “are the key to becoming and succeeding as a data and insight-driven business." Becoming Google Cloud Certified as a Professional Data Engineer will validate your skills in designing, building and maintaining data processing systems with a qualified aptitude for security, reliability, fault-tolerance and scalability.

Even if you’ve accumulated years of industry experience, taken our data and machine learning training and reviewed the documentation, it's still helpful to gauge your readiness for our Data Engineer certification before test day. Our practice exam consists of 20 questions that reflect the format, level and scope of the actual exam. Your score will help you decide if you need more preparation. After completing the practice exam, you’ll receive immediate feedback about which questions you answered correctly or incorrectly so you can spend more time practicing the areas you find challenging.

The following question is similar to what you’ll see on the practice exam:

If your answer was Google Cloud Bigtable, you answered correctly.

Our practice exam, available at no charge, has no built-in time limit, so we recommend you simulate exam conditions and time your session to 45 minutes. Keep in mind, the Data Engineer certification exam has more questions and has a time limit of 120 minutes. The questions on the practice exam will not appear on the certification exam, though they may seem similar.

If you feel like you might need a bit more instruction, consider enrolling in the Data Engineering Specialization on Coursera. This series of five courses is rich in lab experience, providing you with more opportunities to work within a GCP environment. And if you want even more hands-on learning time, check out the Qwiklabs Data Engineering Quest, a quick and easy way to access GCP so you can get more time learning and practicing in an isolated, convenient working environment, at your own pace.